Reliability of a k-out-of-n System with Common-cause Failures Using Multivariate Exponential Distribution

Yuge, Tetsushi; Megumi, Maruyama; Yanagi, Shigeru

doi:10.1016/j.procs.2016.08.101

Cited by 32 publications

(19 citation statements)

References 3 publications

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“…CCF refers to the system failures that are caused by two parts or more than two parts are broken‐down simultaneously because of same influence factors. These common causes include extreme environmental conditions, external loads, incorrect services, and design defects . Usually, there are three assumptions for CCF problem of a two‐state system: The system and its components only have two states: “work” or “complete failure”; CCF only has two possibilities: “appear” or “not appear”; CCFs should be independent. There are two kinds of modeling methods: explicit model and implicit model .…”

Section: Ccf Modeling In Reliability Analysis Of Uncertain Systemmentioning

confidence: 99%

“…This paper employs explicit model. In general, α factor model, β factor model and multiple Greek letter (MGL) model can be used to deal with CCF events α factor model. α factor model is used to analyze CCF problem, and its probability is related to component's independent failure probability and total failure probability.…”

Section: Ccf Modeling In Reliability Analysis Of Uncertain Systemmentioning

confidence: 99%

“…These common causes include extreme environmental conditions, external loads, incorrect services, and design defects. 33,34 Usually, there are three assumptions for CCF problem of a two-state system: There are two kinds of modeling methods: explicit model and implicit model. 35 This paper employs explicit model.…”

Section: Ccf Modeling In Reliability Analysis Of Uncertain Systemmentioning

confidence: 99%

“…In general, α factor model, β factor model and multiple Greek letter (MGL) model can be used to deal with CCF events. 34 1. α factor model.…”

Section: Ccf Modeling In Reliability Analysis Of Uncertain Systemmentioning

confidence: 99%

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Application of discrete‐time Bayesian network on reliability analysis of uncertain system with common cause failure

Song

Cheng

et al. 2018

Quality & Reliability Eng

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With the increasing complexity of engineering systems, reliability analysis and evaluation of systems with traditional methods can't meet practical engineering requirements. Based on limited experimental conditions, lack of data, complex structure models, insufficient cognitive abilities, and many other issues, people have to consider many uncertain factors in system reliability research. Besides, common cause failure (CCF) has become an important factor of system failure. In this paper, a discrete‐time Bayesian network (DTBN) associated with an eight‐rotor unmanned aerial vehicle (UAV) system is presented to discuss above problems. In this approach, the system is assumed as a two‐state system. After that, interval analysis theory is employed to deal with uncertainty. We consider the four sets of auxiliary propellers in the auxiliary power group as a 3/8 voting system, and β factor model is used to process CCF in the auxiliary power group. The proposed methods prove the validity of proposing interval analysis theory to solve uncertain problems and it is necessary to consider reducing or avoiding CCFs in system.

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Section: Ccf Modeling In Reliability Analysis Of Uncertain Systemmentioning

confidence: 99%

Section: Ccf Modeling In Reliability Analysis Of Uncertain Systemmentioning

confidence: 99%

Section: Ccf Modeling In Reliability Analysis Of Uncertain Systemmentioning

confidence: 99%

“…In general, α factor model, β factor model and multiple Greek letter (MGL) model can be used to deal with CCF events. 34 1. α factor model.…”

Section: Ccf Modeling In Reliability Analysis Of Uncertain Systemmentioning

confidence: 99%

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Application of discrete‐time Bayesian network on reliability analysis of uncertain system with common cause failure

Song

Cheng

et al. 2018

Quality & Reliability Eng

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“…The problem of reliability evaluation in a system failing according to a MO model has been addressed by different techniques. Analytical expressions can be obtained for specific topologies, for example, for the k-out-of-n system (see [11]- [13]), but for general networks, even in the exchangeable case, we only rely on simulation techniques [14]. Up to our knowledge, the only work dealing with the nonexchangeable case is [15], that proposed different rare-event simulation techniques to compute the static network reliability.…”

Section: Introductionmentioning

confidence: 99%

On the Marshall–Olkin Copula Model for Network Reliability Under Dependent Failures

et al. 2019

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The Marshall-Olkin (MO) copula model has emerged as the standard tool for capturing dependency between components in failure analysis in reliability. In this model shocks arise at exponential random times, that affect one or several components inducing a natural correlation in the failure process. However, because the number of parameter of the model grows exponentially with the number of components, MO suffers of the "curse of dimensionality". MO models are usually intended to be applied to design a network before its construction, therefore it is natural to assume that only partial information about failure behavior can be gathered, mostly from similar existing networks. To construct such a MO model, we propose an optimization approach to define the shock's parameters in the MO copula, in order to match marginal failures probabilities and correlations between these failures. To deal with the exponential number of parameters of this problem, we use a column-generation technique. We also discuss additional criteria that can be incorporated to obtain a suitable model. Our computational experiments show that the resulting MO model produces a close estimation of the network reliability, especially when the correlation between component failures is significant. Index TermsReliability, copula theory, optimization methods, failure analysis, network design.O. Matus, J. Barrera and E. Moreno are with the Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Santiago, Chile. G. Rubino is with INRIA Rennes -Bretagne Atlantique, Rennes, France.

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The Pliant Probability Distribution Family

Dombi

Jónás

2020

Advances in the Theory of Probabilistic and Fuzzy Data Scientific Methods With Applications

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Reliability of a k-out-of-n System with Common-cause Failures Using Multivariate Exponential Distribution

Cited by 32 publications

References 3 publications

Application of discrete‐time Bayesian network on reliability analysis of uncertain system with common cause failure

Application of discrete‐time Bayesian network on reliability analysis of uncertain system with common cause failure

On the Marshall–Olkin Copula Model for Network Reliability Under Dependent Failures

The Pliant Probability Distribution Family

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